# SOCR Distributome Project

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===Background=== | ===Background=== | ||

- | Probability mass (for discrete processes) or probability density (for continuous processes) functions describe the likelihood of random variables (observation of a process) to take certain values. For instance, we can assign a probability distribution functions to each possible value of discrete and continuous processes, e.g., rolling a hexagonal (6-face) die or picking a random number in the range [0:1]. Most (but not all) probability mass, density and distribution functions have analytical representation, can be computed efficiently, and have specific properties. Exploring the intrinsic distribution characteristics and the intricate relations between different probability distributions is of great importance in many (all) areas of scientific | + | Probability mass (for discrete processes) or probability density (for continuous processes) functions describe the likelihood of random variables (observation of a process) to take certain values. For instance, we can assign a probability distribution functions to each possible value of discrete and continuous processes, e.g., rolling a hexagonal (6-face) die or picking a random number in the range [0:1]. Most (but not all) probability mass, density and distribution functions have analytical representation, can be computed efficiently, and have specific properties. Exploring the intrinsic distribution characteristics and the intricate relations between different probability distributions is of great importance in many (all) areas of scientific exploration. |

===Project Goals=== | ===Project Goals=== |

## Current revision as of 19:32, 6 April 2013

## Contents |

## SOCR Project - SOCR Distributome Project

### Background

Probability mass (for discrete processes) or probability density (for continuous processes) functions describe the likelihood of random variables (observation of a process) to take certain values. For instance, we can assign a probability distribution functions to each possible value of discrete and continuous processes, e.g., rolling a hexagonal (6-face) die or picking a random number in the range [0:1]. Most (but not all) probability mass, density and distribution functions have analytical representation, can be computed efficiently, and have specific properties. Exploring the intrinsic distribution characteristics and the intricate relations between different probability distributions is of great importance in many (all) areas of scientific exploration.

### Project Goals

The Probability Distributome Project aims to provide an intuitive, efficient, portable and extensible infrastructure for exploring, navigating, discovering and expanding the knowledge (meta-data) about the internal distribution properties and characteristics, and inter-distributional relations.

This specific project aims to design, implement, test and validate 2 new features of the Probability Distributome - Enable the Distributome Editor functionality, and Redesign Distributome XML database to enable Bib TeX reference citations.

### Project Specifics

#### Project Source

- Editor Prototype
- Navigator
- JavaScripts: protovis-r3.2.js, common.js, distributome.js, jquery.js, splitter.js, xml2json.js, editor.js
- XML Database: Distributome.xml, Distributome.xsd, and Distributome.xml.html.
- The complete Distributome Project source-code is hosted at GoogleCode.

#### Tasks

- (Easy) Enable the Editor functionality (DistributomeEditor.html, XML_Editor button) in the main Navigator window (DistributomeNavigator.html).
- (Creative) Enable the drag-and-drop functionality for selecting in the graph a node/distribution or an edge/relation and dropping them to populate the XML editor fields.
- (Easy) Test that newly-generated XML can be saved and consumed by the Navigator and displayed appropriately in the graph.
- (Creative) Review this slight redesign spec of the XML DB (Distributome.xml) and the suggested solution and see if you can implement a pilot that utilizes the new pair of XML(nodes/edges)+BiBTeX(references), replacing the current XML DB (Distributome.xml). Note that the project spec page already includes a specific solution; we just need to think about the packaging of the solution with the new XML/BiBTeX format.
- (Creative) Consider the redesign of the Editor according to these schematic diagrams Fig1 and Fig2. Consider designing a lightweight HTML5/JQuery/JavaScript and explore the smooth horizontal and vertical tabs (e.g., dynamic tabs).
- (Creative Open-ended) A complete redesign of the entire Distributome website - e.g., using Bootstrap 2+, V2 dynamic panels, etc.
- Additional tasks - these are creative bonus tasks that can be completed as time permits
- Unify the 4 Distributome Searches (web-site, blog, wiki, XML DB).
- Save-View: Ability to save a specific View of the Distributome Navigator for others to see/publish (perhaps via the Preferences functionality, below).
- Pref XML file: Allow user specifications of node/edge/selected/properties, color-maps, etc.

## References

- Current Distributome XML Database and XSD Model
- Distributome Prototype (Applet)
- Leemis and McQueston's TAS Distrubutions Article (2008)
- Song's IIE Transactions Distribution Relations Paper (2004)

## See also

- Distributome Wiki Page
- Try to think of converting the Distributome HTML-embedded applet into a client-site executable Java application using Java Web-Start (see the example with Cytoscape).
- A Distribution-relationships chart
- A list of probability distributions
- A list of SOCR probability distributions
- XML DOM MathJax Parsing HTML Examples
- JavaScript By-Example Tutorial and JavaScript Object API

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